R Notebook

Run download_data.Rmd and percentage_of_regional_richness.Rmd First!

merlin_city_data <- fetch_city_data_for('merlin')

── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"

merlin_city_data

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ tibble  3.1.2     ✓ dplyr   1.0.7
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
✓ purrr   0.3.4     
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::combine()  masks randomForest::combine()
x dplyr::filter()   masks stats::filter()
x dplyr::lag()      masks stats::lag()
x ggplot2::margin() masks randomForest::margin()

merlin_city_data_fixed <- rfImpute(response ~ ., merlin_city_data)

     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    16.77    93.05 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    16.31    90.48 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    17.04    94.52 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    16.38    90.88 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |     17.2    95.43 |

merlin_city_data_fixed

randomForest(response ~ ., merlin_city_data_fixed)

Call:
 randomForest(formula = response ~ ., data = merlin_city_data_fixed) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 17

          Mean of squared residuals: 16.40603
                    % Var explained: 8.99

select_variables_from_random_forest(merlin_city_data_fixed)

 [1] "merlin_pool_size"                                        "realm"                                                   "biome_name"                                             
 [4] "happiness_positive_effect"                               "rainfall_monthly_min"                                    "region_20km_urban"                                      
 [7] "temperature_annual_average"                              "region_20km_elevation_delta"                             "region_20km_cultivated"                                 
[10] "temperature_monthly_min"                                 "region_50km_urban"                                       "happiness_future_life"                                  
[13] "region_50km_elevation_delta"                             "shrubs"                                                  "permanent_water"                                        
[16] "city_gdp_per_population"                                 "region_100km_cultivated"                                 "region_50km_cultivated"                                 
[19] "region_50km_average_pop_density"                         "happiness_negative_effect"                               "region_100km_urban"                                     
[22] "region_100km_elevation_delta"                            "region_20km_average_pop_density"                         "share_of_population_within_400m_of_open_space"          
[25] "city_average_pop_density"                                "herbaceous_wetland"                                      "rainfall_annual_average"                                
[28] "region_100km_average_pop_density"                        "temperature_monthly_max"                                 "rainfall_monthly_max"                                   
[31] "mean_population_exposure_to_pm2_5_2019"                  "city_max_pop_density"                                    "city_elevation_delta"                                   
[34] "cultivated"                                              "city_mean_elevation"                                     "percentage_urban_area_as_open_public_spaces"            
[37] "herbaceous_vegetation"                                   "urban"                                                   "population_growth"                                      
[40] "percentage_urban_area_as_open_public_spaces_and_streets" "open_forest"                                             "percentage_urban_area_as_streets"                       
[43] "closed_forest"                                          

select_variables_from_random_forest(merlin_city_data_fixed_single_scale)

 [1] "merlin_pool_size"                                        "realm"                                                   "biome_name"                                             
 [4] "happiness_positive_effect"                               "rainfall_monthly_min"                                    "temperature_annual_average"                             
 [7] "region_20km_elevation_delta"                             "happiness_future_life"                                   "temperature_monthly_min"                                
[10] "permanent_water"                                         "shrubs"                                                  "region_20km_urban"                                      
[13] "city_gdp_per_population"                                 "region_20km_cultivated"                                  "happiness_negative_effect"                              
[16] "share_of_population_within_400m_of_open_space"           "city_average_pop_density"                                "temperature_monthly_max"                                
[19] "region_50km_average_pop_density"                         "herbaceous_wetland"                                      "rainfall_annual_average"                                
[22] "rainfall_monthly_max"                                    "city_max_pop_density"                                    "mean_population_exposure_to_pm2_5_2019"                 
[25] "percentage_urban_area_as_open_public_spaces"             "city_mean_elevation"                                     "cultivated"                                             
[28] "city_elevation_delta"                                    "urban"                                                   "population_growth"                                      
[31] "percentage_urban_area_as_open_public_spaces_and_streets" "open_forest"                                             "percentage_urban_area_as_streets"                       
[34] "closed_forest"                                          

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size")])

[1] "Mean  18.3189688617708 , SD:  0.19715466506265 , Mean + SD:  18.5161235268334"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm")])

[1] "Mean  13.869770185836 , SD:  0.140525627740579 , Mean + SD:  14.0102958135766"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name")])

[1] "Mean  14.1475771572531 , SD:  0.185069369813484 , Mean + SD:  14.3326465270665"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect")])

[1] "Mean  14.5546911926491 , SD:  0.235672741470548 , Mean + SD:  14.7903639341196"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min")])

[1] "Mean  14.5623773013355 , SD:  0.230191728097893 , Mean + SD:  14.7925690294334"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average")])

[1] "Mean  14.6428483218659 , SD:  0.195267494460886 , Mean + SD:  14.8381158163268"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta")])

[1] "Mean  15.0077588158041 , SD:  0.249911538143371 , Mean + SD:  15.2576703539475"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life")])

[1] "Mean  14.8200125391895 , SD:  0.269860068851267 , Mean + SD:  15.0898726080408"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min")])

[1] "Mean  14.8088870728519 , SD:  0.251983887288602 , Mean + SD:  15.0608709601405"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water")])

[1] "Mean  15.052305495549 , SD:  0.267727808160636 , Mean + SD:  15.3200333037096"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs")])

[1] "Mean  15.1497081412429 , SD:  0.216072196026799 , Mean + SD:  15.3657803372697"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs", "region_20km_urban")])

[1] "Mean  15.1341979237493 , SD:  0.214956656108439 , Mean + SD:  15.3491545798577"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs", "region_20km_urban", "city_gdp_per_population")])

[1] "Mean  15.10571746549 , SD:  0.268032278025023 , Mean + SD:  15.373749743515"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs", "region_20km_urban", "city_gdp_per_population", "region_20km_cultivated")])

[1] "Mean  15.2546575720185 , SD:  0.215319469958443 , Mean + SD:  15.4699770419769"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs", "region_20km_urban", "city_gdp_per_population", "region_20km_cultivated", "happiness_negative_effect")])

[1] "Mean  15.2900691333102 , SD:  0.210966597605062 , Mean + SD:  15.5010357309153"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs", "region_20km_urban", "city_gdp_per_population", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space")])

[1] "Mean  15.4060817119522 , SD:  0.266427524373179 , Mean + SD:  15.6725092363253"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs", "region_20km_urban", "city_gdp_per_population", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "city_average_pop_density")])

[1] "Mean  15.6023584170658 , SD:  0.278933771668399 , Mean + SD:  15.8812921887342"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs", "region_20km_urban", "city_gdp_per_population", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "city_average_pop_density", "temperature_monthly_max")])

[1] "Mean  15.6959782056278 , SD:  0.253663950252537 , Mean + SD:  15.9496421558804"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs", "region_20km_urban", "city_gdp_per_population", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "city_average_pop_density", "temperature_monthly_max", "region_50km_average_pop_density")])

[1] "Mean  15.690244014838 , SD:  0.237713920089565 , Mean + SD:  15.9279579349276"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs", "region_20km_urban", "city_gdp_per_population", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "city_average_pop_density", "temperature_monthly_max", "region_50km_average_pop_density", "herbaceous_wetland")])

[1] "Mean  15.8068171344378 , SD:  0.288841008803903 , Mean + SD:  16.0956581432417"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "happiness_positive_effect", "rainfall_monthly_min", "temperature_annual_average", "region_20km_elevation_delta", "happiness_future_life", "temperature_monthly_min", "permanent_water", "shrubs", "region_20km_urban", "city_gdp_per_population", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "city_average_pop_density", "temperature_monthly_max", "region_50km_average_pop_density", "herbaceous_wetland", "rainfall_annual_average")])

[1] "Mean  15.8970003444034 , SD:  0.253898577060915 , Mean + SD:  16.1508989214643"

“merlin_pool_size”, “realm”

birdlife_city_data <- fetch_city_data_for('birdlife')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"

birdlife_city_data

birdlife_city_data_fixed <- rfImpute(response ~ ., birdlife_city_data)

     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.543    87.75 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |     5.44    86.12 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.466    86.52 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.393    85.37 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |      5.4    85.48 |

birdlife_city_data_fixed

select_variables_from_random_forest(birdlife_city_data_fixed)

 [1] "population_growth"                                       "birdlife_pool_size"                                      "region_100km_cultivated"                                
 [4] "biome_name"                                              "region_20km_average_pop_density"                         "rainfall_monthly_min"                                   
 [7] "region_50km_cultivated"                                  "permanent_water"                                         "region_50km_average_pop_density"                        
[10] "rainfall_monthly_max"                                    "percentage_urban_area_as_open_public_spaces"             "temperature_monthly_min"                                
[13] "mean_population_exposure_to_pm2_5_2019"                  "region_20km_cultivated"                                  "shrubs"                                                 
[16] "region_100km_average_pop_density"                        "region_100km_urban"                                      "temperature_annual_average"                             
[19] "region_20km_elevation_delta"                             "percentage_urban_area_as_open_public_spaces_and_streets" "region_20km_urban"                                      
[22] "percentage_urban_area_as_streets"                        "city_average_pop_density"                                "open_forest"                                            
[25] "region_50km_elevation_delta"                             "region_50km_urban"                                       "share_of_population_within_400m_of_open_space"          
[28] "temperature_monthly_max"                                 "realm"                                                   "city_max_pop_density"                                   
[31] "happiness_future_life"                                   "rainfall_annual_average"                                 "city_elevation_delta"                                   
[34] "city_gdp_per_population"                                 "happiness_negative_effect"                               "cultivated"                                             
[37] "closed_forest"                                           "city_mean_elevation"                                     "happiness_positive_effect"                              
[40] "herbaceous_wetland"                                      "region_100km_elevation_delta"                            "urban"                                                  
[43] "herbaceous_vegetation"                                  

select_variables_from_random_forest(birdlife_city_data_fixed_single_scale)

 [1] "population_growth"                                       "birdlife_pool_size"                                      "region_100km_cultivated"                                
 [4] "biome_name"                                              "rainfall_monthly_min"                                    "region_20km_average_pop_density"                        
 [7] "percentage_urban_area_as_open_public_spaces"             "permanent_water"                                         "temperature_monthly_min"                                
[10] "rainfall_monthly_max"                                    "mean_population_exposure_to_pm2_5_2019"                  "region_100km_urban"                                     
[13] "temperature_annual_average"                              "shrubs"                                                  "region_20km_elevation_delta"                            
[16] "percentage_urban_area_as_open_public_spaces_and_streets" "share_of_population_within_400m_of_open_space"           "open_forest"                                            
[19] "percentage_urban_area_as_streets"                        "city_elevation_delta"                                    "realm"                                                  
[22] "temperature_monthly_max"                                 "rainfall_annual_average"                                 "city_max_pop_density"                                   
[25] "happiness_negative_effect"                               "happiness_future_life"                                   "city_gdp_per_population"                                
[28] "closed_forest"                                           "city_mean_elevation"                                     "cultivated"                                             
[31] "happiness_positive_effect"                               "herbaceous_wetland"                                      "urban"                                                  
[34] "herbaceous_vegetation"                                  

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth")])

[1] "Mean  6.36812514690974 , SD:  0.0699893081073382 , Mean + SD:  6.43811445501708"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size")])

[1] "Mean  5.520498615513 , SD:  0.0795561100386087 , Mean + SD:  5.60005472555161"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated")])

[1] "Mean  5.04371386558532 , SD:  0.0866595044307178 , Mean + SD:  5.13037337001603"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name")])

[1] "Mean  5.00607385388074 , SD:  0.0749724456600396 , Mean + SD:  5.08104629954078"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min")])

[1] "Mean  4.94875983465646 , SD:  0.0733050112033546 , Mean + SD:  5.02206484585981"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density")])

[1] "Mean  4.89408091448459 , SD:  0.0723301151293599 , Mean + SD:  4.96641102961395"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces")])

[1] "Mean  4.91259542900125 , SD:  0.0832006407574059 , Mean + SD:  4.99579606975865"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water")])

[1] "Mean  4.81573303443048 , SD:  0.0905714601252137 , Mean + SD:  4.90630449455569"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min")])

[1] "Mean  4.82051400444731 , SD:  0.0754282936278812 , Mean + SD:  4.8959422980752"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max")])

[1] "Mean  4.8265637096805 , SD:  0.0818177577747967 , Mean + SD:  4.90838146745529"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019")])

[1] "Mean  4.84852474163507 , SD:  0.0728234804176173 , Mean + SD:  4.92134822205268"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban")])

[1] "Mean  4.821154259606 , SD:  0.0859581869181102 , Mean + SD:  4.90711244652411"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "temperature_annual_average")])

[1] "Mean  4.87739843945672 , SD:  0.0914594075983258 , Mean + SD:  4.96885784705504"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "temperature_annual_average", "shrubs")])

[1] "Mean  4.90611791522006 , SD:  0.0795348713685267 , Mean + SD:  4.98565278658859"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "temperature_annual_average", "shrubs", "region_20km_elevation_delta")])

[1] "Mean  4.95168298717818 , SD:  0.0757784972012371 , Mean + SD:  5.02746148437942"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "temperature_annual_average", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets")])

[1] "Mean  4.9654707422419 , SD:  0.0849784635995637 , Mean + SD:  5.05044920584146"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "temperature_annual_average", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space")])

[1] "Mean  5.00044689211819 , SD:  0.0968048521998649 , Mean + SD:  5.09725174431806"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "temperature_annual_average", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "open_forest")])

[1] "Mean  5.05554461949528 , SD:  0.0870487758868316 , Mean + SD:  5.14259339538211"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "temperature_annual_average", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "open_forest", "percentage_urban_area_as_streets")])

[1] "Mean  5.07578877604463 , SD:  0.0918425932588158 , Mean + SD:  5.16763136930344"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "temperature_annual_average", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "open_forest", "percentage_urban_area_as_streets", "city_elevation_delta")])

[1] "Mean  5.0924686777348 , SD:  0.085826973830433 , Mean + SD:  5.17829565156523"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "percentage_urban_area_as_open_public_spaces", "permanent_water", "temperature_monthly_min", "rainfall_monthly_max", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "temperature_annual_average", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "open_forest", "percentage_urban_area_as_streets", "city_elevation_delta", "realm")])

[1] "Mean  5.08565671537218 , SD:  0.0967755533948937 , Mean + SD:  5.18243226876707"

“population_growth”, “birdlife_pool_size”, “region_100km_cultivated”, “biome_name”, “rainfall_monthly_min”, “region_20km_average_pop_density”, “percentage_urban_area_as_open_public_spaces”, “permanent_water”

select_variables_from_random_forest(either_city_data_fixed)

 [1] "either_pool_size"                                        "population_growth"                                       "region_100km_cultivated"                                
 [4] "region_20km_average_pop_density"                         "realm"                                                   "region_50km_cultivated"                                 
 [7] "region_50km_average_pop_density"                         "shrubs"                                                  "biome_name"                                             
[10] "percentage_urban_area_as_open_public_spaces"             "rainfall_monthly_min"                                    "region_20km_cultivated"                                 
[13] "permanent_water"                                         "temperature_monthly_min"                                 "region_100km_average_pop_density"                       
[16] "region_20km_elevation_delta"                             "mean_population_exposure_to_pm2_5_2019"                  "region_20km_urban"                                      
[19] "region_50km_elevation_delta"                             "city_average_pop_density"                                "rainfall_monthly_max"                                   
[22] "temperature_annual_average"                              "percentage_urban_area_as_open_public_spaces_and_streets" "city_elevation_delta"                                   
[25] "share_of_population_within_400m_of_open_space"           "temperature_monthly_max"                                 "city_max_pop_density"                                   
[28] "region_50km_urban"                                       "cultivated"                                              "region_100km_urban"                                     
[31] "city_gdp_per_population"                                 "happiness_future_life"                                   "city_mean_elevation"                                    
[34] "herbaceous_wetland"                                      "rainfall_annual_average"                                 "happiness_positive_effect"                              
[37] "region_100km_elevation_delta"                            "open_forest"                                             "percentage_urban_area_as_streets"                       
[40] "urban"                                                   "happiness_negative_effect"                               "herbaceous_vegetation"                                  
[43] "closed_forest"                                          

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size")])

[1] "Mean  4.68107781471717 , SD:  0.0529585708192306 , Mean + SD:  4.7340363855364"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth")])

[1] "Mean  4.20940583004367 , SD:  0.0492022724335224 , Mean + SD:  4.25860810247719"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated")])

[1] "Mean  4.09576877863183 , SD:  0.0636465813145703 , Mean + SD:  4.1594153599464"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density")])

[1] "Mean  3.76538827378209 , SD:  0.0527649118617442 , Mean + SD:  3.81815318564383"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm")])

[1] "Mean  3.63107352271867 , SD:  0.0573871316220201 , Mean + SD:  3.68846065434069"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name")])

[1] "Mean  3.89488443708114 , SD:  0.0610036459159636 , Mean + SD:  3.9558880829971"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs")])

[1] "Mean  3.89553458306318 , SD:  0.0689881300982639 , Mean + SD:  3.96452271316145"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces")])

[1] "Mean  3.99073515712685 , SD:  0.0651760898335403 , Mean + SD:  4.05591124696039"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min")])

[1] "Mean  4.04347993514867 , SD:  0.0769460323356499 , Mean + SD:  4.12042596748432"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min")])

[1] "Mean  4.05247857511242 , SD:  0.0758878349158813 , Mean + SD:  4.1283664100283"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta")])

[1] "Mean  4.06957100667791 , SD:  0.0683291852955216 , Mean + SD:  4.13790019197343"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta", "permanent_water")])

[1] "Mean  4.09798181582785 , SD:  0.074704438841254 , Mean + SD:  4.17268625466911"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta", "permanent_water", "mean_population_exposure_to_pm2_5_2019")])

[1] "Mean  4.12814710453946 , SD:  0.0766454973906898 , Mean + SD:  4.20479260193015"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta", "permanent_water", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average")])

[1] "Mean  4.16715002097077 , SD:  0.0657808913892767 , Mean + SD:  4.23293091236005"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta", "permanent_water", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "region_20km_urban")])

[1] "Mean  4.16087925592971 , SD:  0.0848533009464374 , Mean + SD:  4.24573255687614"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta", "permanent_water", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "region_20km_urban", "percentage_urban_area_as_open_public_spaces_and_streets")])

[1] "Mean  4.22423118810479 , SD:  0.0683093368105821 , Mean + SD:  4.29254052491537"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta", "permanent_water", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "region_20km_urban", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space")])

[1] "Mean  4.26380999451308 , SD:  0.0809731884653065 , Mean + SD:  4.34478318297839"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta", "permanent_water", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "region_20km_urban", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max")])

[1] "Mean  4.30231663141395 , SD:  0.0651782253427885 , Mean + SD:  4.36749485675674"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta", "permanent_water", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "region_20km_urban", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_elevation_delta")])

[1] "Mean  4.29139164763849 , SD:  0.0710967981200039 , Mean + SD:  4.3624884457585"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta", "permanent_water", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "region_20km_urban", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_elevation_delta", "herbaceous_wetland")])

[1] "Mean  4.33184364849796 , SD:  0.0785950234481419 , Mean + SD:  4.4104386719461"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "shrubs", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "temperature_monthly_min", "region_20km_elevation_delta", "permanent_water", "mean_population_exposure_to_pm2_5_2019", "temperature_annual_average", "region_20km_urban", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "rainfall_monthly_max", "city_elevation_delta", "herbaceous_wetland", "temperature_monthly_max")])

[1] "Mean  4.38537704857627 , SD:  0.0710889177339408 , Mean + SD:  4.45646596631021"

“either_pool_size”, “population_growth”, “region_100km_cultivated”, “region_20km_average_pop_density”, “realm”

select_variables_from_random_forest(both_city_data_fixed)

 [1] "both_pool_size"                                          "temperature_annual_average"                              "happiness_negative_effect"                              
 [4] "temperature_monthly_min"                                 "permanent_water"                                         "region_100km_cultivated"                                
 [7] "region_20km_urban"                                       "region_50km_cultivated"                                  "region_20km_cultivated"                                 
[10] "realm"                                                   "rainfall_monthly_min"                                    "region_50km_elevation_delta"                            
[13] "percentage_urban_area_as_open_public_spaces"             "shrubs"                                                  "happiness_positive_effect"                              
[16] "happiness_future_life"                                   "population_growth"                                       "city_gdp_per_population"                                
[19] "region_100km_elevation_delta"                            "region_20km_average_pop_density"                         "region_100km_urban"                                     
[22] "biome_name"                                              "city_average_pop_density"                                "region_20km_elevation_delta"                            
[25] "share_of_population_within_400m_of_open_space"           "open_forest"                                             "region_50km_urban"                                      
[28] "region_50km_average_pop_density"                         "herbaceous_wetland"                                      "region_100km_average_pop_density"                       
[31] "mean_population_exposure_to_pm2_5_2019"                  "cultivated"                                              "city_elevation_delta"                                   
[34] "city_mean_elevation"                                     "percentage_urban_area_as_open_public_spaces_and_streets" "rainfall_monthly_max"                                   
[37] "temperature_monthly_max"                                 "herbaceous_vegetation"                                   "rainfall_annual_average"                                
[40] "city_max_pop_density"                                    "percentage_urban_area_as_streets"                        "closed_forest"                                          
[43] "urban"                                                  

select_variables_from_random_forest(both_city_data_fixed_single_scale)

 [1] "both_pool_size"                                          "temperature_annual_average"                              "happiness_negative_effect"                              
 [4] "temperature_monthly_min"                                 "permanent_water"                                         "region_20km_urban"                                      
 [7] "region_100km_cultivated"                                 "rainfall_monthly_min"                                    "percentage_urban_area_as_open_public_spaces"            
[10] "realm"                                                   "happiness_positive_effect"                               "population_growth"                                      
[13] "region_50km_elevation_delta"                             "shrubs"                                                  "city_gdp_per_population"                                
[16] "happiness_future_life"                                   "biome_name"                                              "share_of_population_within_400m_of_open_space"          
[19] "open_forest"                                             "herbaceous_wetland"                                      "percentage_urban_area_as_open_public_spaces_and_streets"
[22] "cultivated"                                              "city_mean_elevation"                                     "rainfall_monthly_max"                                   
[25] "temperature_monthly_max"                                 "rainfall_annual_average"                                 "percentage_urban_area_as_streets"                       
[28] "urban"                                                  

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size")])

[1] "Mean  17.0573842982199 , SD:  0.158781230508028 , Mean + SD:  17.2161655287279"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average")])

[1] "Mean  14.2265193200808 , SD:  0.182715311029114 , Mean + SD:  14.4092346311099"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect")])

[1] "Mean  13.2974151408976 , SD:  0.172029531337893 , Mean + SD:  13.4694446722355"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water")])

[1] "Mean  13.6495184580861 , SD:  0.19661723337752 , Mean + SD:  13.8461356914636"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban")])

[1] "Mean  13.6847138614015 , SD:  0.187512025671624 , Mean + SD:  13.8722258870732"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated")])

[1] "Mean  13.7495989636209 , SD:  0.179353121798753 , Mean + SD:  13.9289520854196"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min")])

[1] "Mean  13.9191705203945 , SD:  0.23616544608524 , Mean + SD:  14.1553359664797"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces")])

[1] "Mean  13.9485914160613 , SD:  0.242665314884572 , Mean + SD:  14.1912567309459"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm")])

[1] "Mean  13.8436723181486 , SD:  0.221888792323216 , Mean + SD:  14.0655611104718"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect")])

[1] "Mean  14.1571192594309 , SD:  0.2465479866693 , Mean + SD:  14.4036672461002"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect", "population_growth")])

[1] "Mean  14.2786575486517 , SD:  0.216411051843621 , Mean + SD:  14.4950686004953"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect", "population_growth", "region_50km_elevation_delta")])

[1] "Mean  14.5934288195255 , SD:  0.229445483984334 , Mean + SD:  14.8228743035098"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect", "population_growth", "region_50km_elevation_delta", "shrubs")])

[1] "Mean  14.5951110959139 , SD:  0.243846125678944 , Mean + SD:  14.8389572215928"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect", "population_growth", "region_50km_elevation_delta", "shrubs", "city_gdp_per_population")])

[1] "Mean  14.6975056748365 , SD:  0.215913195328559 , Mean + SD:  14.913418870165"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect", "population_growth", "region_50km_elevation_delta", "shrubs", "city_gdp_per_population", "happiness_future_life")])

[1] "Mean  14.7782428837829 , SD:  0.250968471155811 , Mean + SD:  15.0292113549388"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect", "population_growth", "region_50km_elevation_delta", "shrubs", "city_gdp_per_population", "happiness_future_life", "biome_name")])

[1] "Mean  14.6204321113383 , SD:  0.213853154272812 , Mean + SD:  14.8342852656111"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect", "population_growth", "region_50km_elevation_delta", "shrubs", "city_gdp_per_population", "happiness_future_life", "biome_name", "share_of_population_within_400m_of_open_space")])

[1] "Mean  14.8111407511192 , SD:  0.265464036798428 , Mean + SD:  15.0766047879176"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect", "population_growth", "region_50km_elevation_delta", "shrubs", "city_gdp_per_population", "happiness_future_life", "biome_name", "share_of_population_within_400m_of_open_space", "open_forest")])

[1] "Mean  14.9223527775334 , SD:  0.223852815096881 , Mean + SD:  15.1462055926303"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect", "population_growth", "region_50km_elevation_delta", "shrubs", "city_gdp_per_population", "happiness_future_life", "biome_name", "share_of_population_within_400m_of_open_space", "open_forest", "herbaceous_wetland")])

[1] "Mean  14.9490556934422 , SD:  0.264567696391672 , Mean + SD:  15.2136233898338"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "happiness_negative_effect", "permanent_water", "region_20km_urban", "region_100km_cultivated", "rainfall_monthly_min", "percentage_urban_area_as_open_public_spaces", "realm", "happiness_positive_effect", "population_growth", "region_50km_elevation_delta", "shrubs", "city_gdp_per_population", "happiness_future_life", "biome_name", "share_of_population_within_400m_of_open_space", "open_forest", "herbaceous_wetland", "percentage_urban_area_as_open_public_spaces_and_streets")])

[1] "Mean  15.0828378915462 , SD:  0.249182576727487 , Mean + SD:  15.3320204682737"

“both_pool_size”, “temperature_annual_average”, “happiness_negative_effect”

So….
“merlin_pool_size”, “realm” “population_growth”, “birdlife_pool_size”, “region_100km_cultivated”, “biome_name”, “rainfall_monthly_min”, “region_20km_average_pop_density”, “percentage_urban_area_as_open_public_spaces”, “permanent_water” “either_pool_size”, “population_growth”, “region_100km_cultivated”, “region_20km_average_pop_density”, “realm” “both_pool_size”, “temperature_annual_average”, “happiness_negative_effect”
```r summary(lm(response ~ merlin_pool_size, merlin_city_data_fixed))
```
```
Call: lm(formula = response ~ merlin_pool_size, data = merlin_city_data_fixed)
Residuals: Min 1Q Median 3Q Max -8.3644 -2.2493 -0.3649 1.7804 15.4604
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.205975 0.920945 6.739 4.23e-10 merlin_pool_size -0.022439 0.003134 -7.160 4.71e-11

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.641 on 135 degrees of freedom Multiple R-squared: 0.2752, Adjusted R-squared: 0.2698 F-statistic: 51.26 on 1 and 135 DF, p-value: 4.707e-11

<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3VtbWFyeShsbShyZXNwb25zZSB+IGJpcmRsaWZlX3Bvb2xfc2l6ZSwgYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkKSlcbmBgYCJ9 -->

```r
summary(lm(response ~ birdlife_pool_size, birdlife_city_data_fixed))

Call:
lm(formula = response ~ birdlife_pool_size, data = birdlife_city_data_fixed)

Residuals:
   Min     1Q Median     3Q    Max 
-5.140 -1.330 -0.313  1.034  9.156 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         2.602931   0.625873   4.159 5.65e-05 ***
birdlife_pool_size -0.008789   0.002000  -4.395 2.23e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.368 on 135 degrees of freedom
Multiple R-squared:  0.1252,    Adjusted R-squared:  0.1187 
F-statistic: 19.31 on 1 and 135 DF,  p-value: 2.225e-05

summary(lm(response ~ either_pool_size, either_city_data_fixed))

Call:
lm(formula = response ~ either_pool_size, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8488 -1.0658 -0.3811  0.8665  6.5921 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       3.250304   0.584389   5.562 1.38e-07 ***
either_pool_size -0.009005   0.001546  -5.825 3.99e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.031 on 135 degrees of freedom
Multiple R-squared:  0.2008,    Adjusted R-squared:  0.1949 
F-statistic: 33.92 on 1 and 135 DF,  p-value: 3.99e-08

summary(lm(response ~ both_pool_size, both_city_data_fixed))

Call:
lm(formula = response ~ both_pool_size, data = both_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.9674 -2.7370 -0.3475  1.8439 10.3398 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     5.261657   0.982371   5.356 3.56e-07 ***
both_pool_size -0.024842   0.004396  -5.651 9.08e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.667 on 135 degrees of freedom
Multiple R-squared:  0.1913,    Adjusted R-squared:  0.1853 
F-statistic: 31.94 on 1 and 135 DF,  p-value: 9.076e-08

summary(lm(response ~ region_100km_cultivated, merlin_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.7405 -2.8276 -0.5911  1.5098 18.0590 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)              -0.6281     0.5172  -1.214   0.2267  
region_100km_cultivated   2.3444     1.3805   1.698   0.0918 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.232 on 135 degrees of freedom
Multiple R-squared:  0.02092,   Adjusted R-squared:  0.01366 
F-statistic: 2.884 on 1 and 135 DF,  p-value: 0.09176

summary(lm(response ~ region_100km_cultivated, birdlife_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = birdlife_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4506 -1.5884 -0.3702  1.3865  9.9581 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)              -0.6226     0.3002  -2.074  0.04001 * 
region_100km_cultivated   2.3237     0.8013   2.900  0.00436 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.457 on 135 degrees of freedom
Multiple R-squared:  0.05864,   Adjusted R-squared:  0.05167 
F-statistic: 8.409 on 1 and 135 DF,  p-value: 0.004359

summary(lm(response ~ region_100km_cultivated, either_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.6522 -1.4255 -0.2114  0.9771  6.3724 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)              -0.5459     0.2698  -2.024  0.04499 * 
region_100km_cultivated   2.0373     0.7200   2.830  0.00537 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.207 on 135 degrees of freedom
Multiple R-squared:  0.05599,   Adjusted R-squared:  0.049 
F-statistic: 8.008 on 1 and 135 DF,  p-value: 0.00537

summary(lm(response ~ region_100km_cultivated, both_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = both_city_data_fixed)

Residuals:
   Min     1Q Median     3Q    Max 
-8.439 -2.791 -0.689  1.898 12.088 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)              -0.7221     0.4908  -1.471   0.1436  
region_100km_cultivated   2.6951     1.3099   2.057   0.0416 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.016 on 135 degrees of freedom
Multiple R-squared:  0.0304,    Adjusted R-squared:  0.02322 
F-statistic: 4.233 on 1 and 135 DF,  p-value: 0.04157

ggplot() + 
  geom_point(aes(x = merlin_pool_size, y = region_100km_cultivated), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = birdlife_pool_size, y = region_100km_cultivated), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = either_pool_size, y = region_100km_cultivated), either_city_data_fixed, color = "green") +
  geom_point(aes(x = both_pool_size, y = region_100km_cultivated), both_city_data_fixed, color = "purple")

summary(lm(response ~ population_growth, merlin_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.2751 -2.8391 -0.4272  1.4837 18.4058 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.094091   0.524516   0.179    0.858
population_growth -0.001479   0.005915  -0.250    0.803

Residual standard error: 4.276 on 135 degrees of freedom
Multiple R-squared:  0.0004627, Adjusted R-squared:  -0.006941 
F-statistic: 0.0625 on 1 and 135 DF,  p-value: 0.803

summary(lm(response ~ population_growth, birdlife_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = birdlife_city_data_fixed)

Residuals:
   Min     1Q Median     3Q    Max 
-5.085 -1.538 -0.459  1.240 10.226 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.231365   0.309332   0.748    0.456
population_growth -0.003636   0.003489  -1.042    0.299

Residual standard error: 2.522 on 135 degrees of freedom
Multiple R-squared:  0.007984,  Adjusted R-squared:  0.0006359 
F-statistic: 1.087 on 1 and 135 DF,  p-value: 0.2991

summary(lm(response ~ population_growth, either_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1409 -1.3284 -0.1829  0.8324  6.7919 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.113195   0.278318   0.407    0.685
population_growth -0.001779   0.003139  -0.567    0.572

Residual standard error: 2.269 on 135 degrees of freedom
Multiple R-squared:  0.002374,  Adjusted R-squared:  -0.005016 
F-statistic: 0.3213 on 1 and 135 DF,  p-value: 0.5718

summary(lm(response ~ population_growth, both_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = both_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.1143 -2.5568 -0.7818  2.1289 12.4621 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.188410   0.499736   0.377    0.707
population_growth -0.002961   0.005636  -0.525    0.600

Residual standard error: 4.074 on 135 degrees of freedom
Multiple R-squared:  0.002041,  Adjusted R-squared:  -0.005351 
F-statistic: 0.2761 on 1 and 135 DF,  p-value: 0.6001

summary(lm(response ~ rainfall_monthly_min, merlin_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.2835 -2.9452 -0.4893  1.4983 18.2505 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.191459   0.491332   0.390    0.697
rainfall_monthly_min -0.007481   0.012853  -0.582    0.562

Residual standard error: 4.272 on 135 degrees of freedom
Multiple R-squared:  0.002503,  Adjusted R-squared:  -0.004886 
F-statistic: 0.3387 on 1 and 135 DF,  p-value: 0.5615

summary(lm(response ~ rainfall_monthly_min, birdlife_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = birdlife_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.0114 -1.4084 -0.4231  1.3632 10.6767 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.244199   0.289526   0.843     0.40
rainfall_monthly_min -0.009541   0.007574  -1.260     0.21

Residual standard error: 2.517 on 135 degrees of freedom
Multiple R-squared:  0.01162,   Adjusted R-squared:  0.004298 
F-statistic: 1.587 on 1 and 135 DF,  p-value: 0.2099

summary(lm(response ~ rainfall_monthly_min, either_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1121 -1.3720 -0.2964  0.8111  6.5298 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.219743   0.259756   0.846    0.399
rainfall_monthly_min -0.008586   0.006795  -1.264    0.209

Residual standard error: 2.258 on 135 degrees of freedom
Multiple R-squared:  0.01169,   Adjusted R-squared:  0.004367 
F-statistic: 1.597 on 1 and 135 DF,  p-value: 0.2086

summary(lm(response ~ rainfall_monthly_min, both_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = both_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.0991 -2.8506 -0.8491  1.9009 12.2257 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.30602    0.46742   0.655    0.514
rainfall_monthly_min -0.01196    0.01223  -0.978    0.330

Residual standard error: 4.064 on 135 degrees of freedom
Multiple R-squared:  0.007033,  Adjusted R-squared:  -0.0003223 
F-statistic: 0.9562 on 1 and 135 DF,  p-value: 0.3299

summary(lm(response ~ biome_name, merlin_city_data_fixed))

Call:
lm(formula = response ~ biome_name, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.7663 -2.4594 -0.4676  2.1272 18.4309 

Coefficients:
                                                                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)                                                         -3.2599     4.2666  -0.764   0.4463  
biome_nameDeserts & Xeric Shrublands                                 3.1836     4.4563   0.714   0.4763  
biome_nameFlooded Grasslands & Savannas                              0.6618     5.2255   0.127   0.8994  
biome_nameMangroves                                                  9.3150     5.2255   1.783   0.0771 .
biome_nameMediterranean Forests, Woodlands & Scrub                   3.2643     4.4066   0.741   0.4602  
biome_nameMontane Grasslands & Shrublands                            1.5344     5.2255   0.294   0.7695  
biome_nameTemperate Broadleaf & Mixed Forests                        3.2942     4.3328   0.760   0.4485  
biome_nameTemperate Conifer Forests                                  3.3572     5.2255   0.642   0.5218  
biome_nameTemperate Grasslands, Savannas & Shrublands                4.3835     4.6739   0.938   0.3501  
biome_nameTropical & Subtropical Coniferous Forests                  7.4846     5.2255   1.432   0.1546  
biome_nameTropical & Subtropical Dry Broadleaf Forests               3.7631     4.4164   0.852   0.3958  
biome_nameTropical & Subtropical Grasslands, Savannas & Shrublands   5.9138     4.6739   1.265   0.2081  
biome_nameTropical & Subtropical Moist Broadleaf Forests             2.4622     4.3148   0.571   0.5693  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.267 on 124 degrees of freedom
Multiple R-squared:  0.08597,   Adjusted R-squared:  -0.002489 
F-statistic: 0.9719 on 12 and 124 DF,  p-value: 0.4793

In Summary

Response is related to number of species in regional pool, the more species, the less the percentage of species in the city. Indicating a fixed number of species are able to move into cities. The size of the regional pool is correlated with both the amount of urban and cultivated land cover, both reduce species in the regional pool.

Response is also lower in wet biomes and areas of the world, this is seen through the higher rainfall in the month with least rainfall in the year, and lower percentages in wetter biomes such as flooded grasslands and moist broadleaf forests.

Finally cities with a higher proportion of green public space are less likely to have a low response.